In recent years, automatic surveillance system aided by computer vision technology is gaining attention. Video surveillance system detects the occurrence of abnormal security events by analyzing the behavior of the moving people in monitored video, and effectively notifies the security staff to handle. The basic issues of video surveillance systems, such as, background subtraction, moving object detection and tracking, shadow removal, and so on, are all well researched and documented. High-level event detection, such as, behavior analysis, unattended object detection, loitering detection or jam detection, i.e., automatic and intelligent behavior analysis, is also expected to be in high demand. A steady moving object tracking technology is the basic element of the intelligent video surveillance system.
The measuring range of a single sensor, such as, the field of view (FOV) of a camera, cannot cover the entire environment in surveillance. A camera network with a plurality of cameras is usually designed to exclude overlapping field of view among cameras because of the cost concern. In addition, when the number of cameras increases, the color correction and network structure become complicated. Taiwan Patent Publication No. 200806020 discloses a video tracking technology by using a fixed camera with pre-set priority and a PTZ camera cooperatively tracking an object. When the camera with priority detects moving object, PTZ camera is activated to track the moving object so that the field of view covers the field of view of fixed camera.
Taiwan Patent Publication No. 200708102 discloses a video surveillance system merging data from a plurality of surveillance cameras to monitor a large-area scene, and providing scene map and scale map of the monitored scene, and sensor network model information of the scene to the monitored scene. For example, as shown in FIG. 1, these types of information may be stored in map-FOV image 104, human scale map 108 and camera network model 112, and may be generated and managed by map basic calibrator 102, FOV basic calibrator 106 and camera network model manager 110.
U.S. Pat. No. 7,149,325 discloses a cooperative camera network architecture for recording color characteristic of pedestrians and storing in a database for human identification, where only when the person is in the overlapped part of the cameras, the moving object can be tracked. U.S. Pat. No. 7,394,916 discloses a method for target tracking, aiming at the situation when a human figure appearing in different cameras, comparing the likelihoods of transition of the scene and the other scenes of the previous human figures departing for the basis as human tracking. The likelihoods of transition aim at the blueprint of scene, speed of moving object and the distance to entrances and exits or traffic condition, and are set by the user.
China Patent Publication No. 101,142,593A discloses a method for tracking target in a video sequence. This method compares the changes of appearance feature of the foreground appearing in different cameras. When comparing the different foreground objects, extra comparison is performed when different foreground objects show the state of engagement so as to eliminate the condition that the correct corresponding foreground object cannot be found when the foreground object is in the state of engagement. When comparing different foreground objects in different cameras, the combination of foreground color distribution and edge density information is used to compute the correlation of the foregrounds.
China Patent Publication No. 101,090,485A discloses an image surveillance system and object tracking method, where the functional module of image processing unit 200 is shown as FIG. 2. The image processing unit executes the object detection processing and object tracking processing in detecting moving object of the image. For the tracking processing between different cameras, this unit uses a unique label to correlate the current object and the previous object. When the object tracked is shielded and invisible, the tracking processing will keep the label assigned to the invisible object and the label will be assigned to the object when the object is visible again.
For designing a cross-camera human tracking system, conventionally, manual labeling on corresponding objects is performed by visual inspection in the training phase according to the object color, appearing time, and so on, to find the probability distribution of the different cameras through training samples, and then in the detection phase, the trained probability distribution is used to correlate the cross-camera objects to achieve the cross-camera object tracking.